View Item 

Show simple item record

Single‐molecule enzymology à la Michaelis–Menten
dc.contributor.authorGrima, Ramonen_US
dc.contributor.authorWalter, Nils G.en_US
dc.contributor.authorSchnell, Santiagoen_US
dc.date.accessioned2014-02-11T17:57:14Z
dc.date.available2015-03-02T14:35:33Zen_US
dc.date.issued2014-01en_US
dc.identifier.citationGrima, Ramon; Walter, Nils G.; Schnell, Santiago (2014). "Single‐molecule enzymology à la Michaelis–Menten." FEBS Journal (2): 518-530.en_US
dc.identifier.issn1742-464Xen_US
dc.identifier.issn1742-4658en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/102694
dc.publisherFreemanen_US
dc.publisherWiley Periodicals, Inc.en_US
dc.subject.otherSingle‐Molecule Analysisen_US
dc.subject.otherStochastic Simulationsen_US
dc.subject.otherStochastic Enzyme Kineticsen_US
dc.subject.otherChemical Master Equationen_US
dc.subject.otherDeterministic Rate Equationsen_US
dc.subject.otherEffective Mesoscopic Rate Equationsen_US
dc.subject.otherInitial Rateen_US
dc.subject.otherMichaelis–Menten Reaction Mechanismen_US
dc.subject.otherNoiseen_US
dc.subject.otherParameter Estimationen_US
dc.titleSingle‐molecule enzymology à la Michaelis–Mentenen_US
dc.typeArticleen_US
dc.rights.robotsIndexNoFollowen_US
dc.subject.hlbsecondlevelBiological Chemistryen_US
dc.subject.hlbtoplevelScienceen_US
dc.description.peerreviewedPeer Revieweden_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/102694/1/febs12663.pdf
dc.identifier.doi10.1111/febs.12663en_US
dc.identifier.sourceFEBS Journalen_US
dc.identifier.citedreferenceSanft KR, Gillespie DT & Petzold LR ( 2011 ) Legitimacy of the stochastic Michaelis‐Menten approximation. IET Syst Biol 5, 58 – 69.en_US
dc.identifier.citedreferenceQian H ( 2008 ) Cooperativity and specificity in enzyme kinetics: a single‐molecule time‐based perspective. Biophys J 95, 10 – 17.en_US
dc.identifier.citedreferenceMunsky B & Khammash M ( 2006 ) The finite state projection algorithm for the solution of the chemical master equation. J Chem Phys 124, 044104.en_US
dc.identifier.citedreferenceRao CV & Arkin AP ( 2003 ) Stochastic chemical kinetics and the quasi‐steady‐state assumption: application to the Gillespie algorithm. J Chem Phys 118, 4999 – 5010.en_US
dc.identifier.citedreferenceQian H & Bishop LM ( 2010 ) The chemical master equation approach to nonequilibrium steady‐state of open biochemical systems: linear single‐molecule enzyme kinetics and nonlinear biochemical reaction networks. Int J Mol Sci 11, 3472 – 3500.en_US
dc.identifier.citedreferenceGrima R ( 2009 ) Investigating the robustness of the classical enzyme kinetic equations in small intracellular compartments. BMC Syst Biol 3, 101.en_US
dc.identifier.citedreferenceGe H & Qian H ( 2013 ) Dissipation, generalized free energy, and a self‐consistent nonequilibrium thermodynamics of chemically driven open subsystems. Phys Rev E 87, 062125.en_US
dc.identifier.citedreferenceStefanini MO, McKane AJ & Newman TJ ( 2005 ) Single enzyme pathways and substrate fluctuations. Nonlinearity 18, 1575 – 1595.en_US
dc.identifier.citedreferenceQian H ( 2012 ) Cooperativity in cellular biochemical processes: noise‐enhanced sensitivity, fluctuating enzyme, bistability with nonlinear feedback, and other mechanisms for sigmoidal responses. Annu Rev Biophys 41, 179 – 204.en_US
dc.identifier.citedreferenceDarvey IG & Staff PJ ( 1967 ) The application of the theory of Markov processes to the reversible one substrate‐one intermediate‐one product enzymic mechanism. J Theor Biol 14, 157 – 172.en_US
dc.identifier.citedreferenceQian H & Elson EL ( 2002 ) Single‐molecule enzymology: stochastic Michaelis‐Menten kinetics. Biophys Chem 101–102, 565 – 576.en_US
dc.identifier.citedreferenceBar‐Even A, Noor E, Savir Y, Liebermeister W, Davidi D, Tawfik DS & Milo R ( 2011 ) The moderately efficient enzyme: evolutionary and physicochemical trends shaping enzyme parameters. Biochemistry 50, 4402 – 4410.en_US
dc.identifier.citedreferenceTanaka S, Kerfeld CA, Sawaya MR, Cai F, Heinhorst S, Cannon GC & Yeates TO ( 2008 ) Atomic‐level models of the bacterial carboxysome shell. Science 319, 1083 – 1086.en_US
dc.identifier.citedreferenceYeates TO, Crowley CS & Tanaka S ( 2010 ) Bacterial microcompartment organelles: protein shell structure and evolution. Annu Rev Biophys 39, 185 – 205.en_US
dc.identifier.citedreferenceThomas P, Matuschek H & Grima R ( 2012 ) Computation of biochemical pathway fluctuations beyond the linear noise approximation using iNA. Paper presented at the 2012 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1 – 5.en_US
dc.identifier.citedreferenceXu L & Qu Z ( 2012 ) Roles of protein ubiquitination and degradation kinetics in biological oscillations. PLoS ONE 7, e34616.en_US
dc.identifier.citedreferenceKaern M, Elston TC, Blake WJ & Collins JJ ( 2005 ) Stochasticity in gene expression: from theories to phenotypes. Nat Rev Genet 6, 451 – 464.en_US
dc.identifier.citedreferenceTaniguchi Y, Choi PJ, Li GW, Chen H, Babu M, Hearn J, Emili A & Xie XS ( 2010 ) Quantifying E. coli proteome and transcriptome with single‐molecule sensitivity in single cells. Science 329, 533 – 538.en_US
dc.identifier.citedreferenceShahrezaei V & Swain PS ( 2008 ) Analytical distributions for stochastic gene expression. Proc Natl Acad Sci USA 105, 17256 – 17261.en_US
dc.identifier.citedreferenceCai L, Friedman N & Xie XS ( 2006 ) Stochastic protein expression in individual cells at the single molecule level. Nature 440, 358 – 362.en_US
dc.identifier.citedreferenceOzbudak EM, Thattai M, Kurtser I, Grossman AD & van Oudenaarden A ( 2002 ) Regulation of noise in the expression of a single gene. Nat Genet 31, 69 – 73.en_US
dc.identifier.citedreferenceAnzenbacher P Jr & Palacios MA ( 2009 ) Polymer nanofibre junctions of attolitre volume serve as zeptomole‐scale chemical reactors. Nat Chem 1, 80 – 86.en_US
dc.identifier.citedreferenceKarlsson M, Davidson M, Karlsson R, Karlsson A, Bergenholtz J, Konkoli Z, Jesorka A, Lobovkina T, Hurtig J, Voinova M et al. ( 2004 ) Biomimetic nanoscale reactors and networks. Annu Rev Phys Chem 55, 613 – 649.en_US
dc.identifier.citedreferenceBoys RJ, Wilkinson DJ & Kirkwood TBL ( 2008 ) Bayesian inference for a discretely observed stochastic kinetic model. Stat Comput 18, 125 – 135.en_US
dc.identifier.citedreferenceMoles CG, Mendes P & Banga JR ( 2003 ) Parameter estimation in biochemical pathways: a comparison of global optimization methods. Genome Res 13, 2467 – 2474.en_US
dc.identifier.citedreferenceSamoilov MS & Arkin AP ( 2006 ) Deviant effects in molecular reaction pathways. Nat Biotechnol 24, 1235 – 1240.en_US
dc.identifier.citedreferenceFersht A ( 1999 ) Structure and Mechanism in Protein Science: A Guide to Enzyme Catalysis and Protein Folding. Freeman, New York.en_US
dc.identifier.citedreferenceCornish‐Bowden A ( 2012 ) Fundamentals of Enzyme Kinetics, 4th edn. Wiley‐VCH, Weinheim.en_US
dc.identifier.citedreferenceHenri V ( 1902 ) Théorie générale de l'action de quelques diastases. CR Hebd Acad Sci 135, 916 – 919.en_US
dc.identifier.citedreferenceHenri V ( 1903 ) Lois Générales De L'Action Des Diastases. Hermann, Paris.en_US
dc.identifier.citedreferenceSchnell S, Chappell MJ, Evans ND & Roussel MR ( 2006 ) The mechanism distinguishability problem in biochemical kinetics: the single‐enzyme, single‐substrate reaction as a case study. CR Biol 329, 51 – 61.en_US
dc.identifier.citedreferenceMichaelis L & Menten ML ( 1913 ) Die kinetik der invertinwirkung. Biochem Z 49, 333 – 369.en_US
dc.identifier.citedreferenceSchnell S & Maini PK ( 2003 ) A century of enzyme kinetics: reliability of the K M and v max estimates. Comment Theor Biol 8, 169 – 187.en_US
dc.identifier.citedreferenceZimmerle CT & Frieden C ( 1989 ) Analysis of progress curves by simulations generated by numerical integration. Biochem J 258, 381 – 387.en_US
dc.identifier.citedreferenceDuggleby RG ( 1995 ) Analysis of enzyme progress curves by nonlinear‐regression. Methods Enzymol 249, 61 – 90.en_US
dc.identifier.citedreferenceMarangoni AG ( 2003 ) Enzyme Kinetics: A Modern Approach. Wiley‐Interscience, Hoboken, NJ.en_US
dc.identifier.citedreferenceTurner TE, Schnell S & Burrage K ( 2004 ) Stochastic approaches for modelling in vivo reactions. Comput Biol Chem 28, 165 – 178.en_US
dc.identifier.citedreferenceGrima R & Schnell S ( 2008 ) Modelling reaction kinetics inside cells. Essays Biochem 45, 41 – 56.en_US
dc.identifier.citedreferenceIshihama Y, Schmidt T, Rappsilber J, Mann M, Hartl FU, Kerner MJ & Frishman D ( 2008 ) Protein abundance profiling of the Escherichia coli cytosol. BMC Genomics 9, 102.en_US
dc.identifier.citedreferenceWalter NG, Huang CY, Manzo AJ & Sobhy MA ( 2008 ) Do‐it‐yourself guide: how to use the modern single‐molecule toolkit. Nat Methods 5, 475 – 489.en_US
dc.identifier.citedreferenceBartholomay AF ( 1962 ) A stochastic approach to statistical kinetics with application to enzyme kinetics. Biochemistry 1, 223 – 230.en_US
dc.identifier.citedreferencevan Kampen NG ( 2007 ) Stochastic Processes in Physics and Chemistry, 3rd edn. North‐Holland, Amsterdam.en_US
dc.identifier.citedreferenceXie XS & Trautman JK ( 1998 ) Optical studies of single molecules at room temperature. Annu Rev Phys Chem 49, 441 – 480.en_US
dc.identifier.citedreferenceGrohmann D, Werner F & Tinnefeld P ( 2013 ) Making connections‐strategies for single molecule fluorescence biophysics. Curr Opin Chem Biol 17, 691 – 698.en_US
dc.identifier.citedreferenceCoelho M, Maghelli N & Tolić‐Nørrelykke IM ( 2013 ) Single‐molecule imaging in vivo: the dancing building blocks of the cell. Integr Biol 5, 748 – 758.en_US
dc.identifier.citedreferenceXia T, Li N & Fang X ( 2013 ) Single‐molecule fluorescence imaging in living cells. Annu Rev Phys Chem 64, 459 – 480.en_US
dc.identifier.citedreferencePuchner EM & Gaub HE ( 2012 ) Single‐molecule mechanoenzymatics. Annu Rev Biophys 41, 497 – 518.en_US
dc.identifier.citedreferenceElson EL ( 2011 ) Fluorescence correlation spectroscopy: past, present, future. Biophys J 101, 2855 – 2870.en_US
dc.identifier.citedreferenceHarriman OLJ & Leake MC ( 2011 ) Single molecule experimentation in biological physics: exploring the living component of soft condensed matter one molecule at a time. J Phys‐Condens Matter 23, 503101.en_US
dc.identifier.citedreferenceLi GW & Xie XS ( 2011 ) Central dogma at the single‐molecule level in living cells. Nature 475, 308 – 315.en_US
dc.identifier.citedreferenceFeynman RP ( 1961 ) Miniaturization. Reinhold, New York.en_US
dc.identifier.citedreferenceWalter NG ( 2010 ) Single molecule tools: fluorescence based approaches Part A. Preface. Methods Enzymol 472, xxi – xxii.en_US
dc.identifier.citedreferenceJain A, Liu R, Ramani B, Arauz E, Ishitsuka Y, Ragunathan K, Park J, Chen J, Xiang YK & Ha T ( 2011 ) Probing cellular protein complexes using single‐molecule pull‐down. Nature 473, 484 – 488.en_US
dc.identifier.citedreferenceHoskins AA, Gelles J & Moore MJ ( 2011 ) New insights into the spliceosome by single molecule fluorescence microscopy. Curr Opin Chem Biol 15, 864 – 870.en_US
dc.identifier.citedreferenceEnglish BP, Min W, van Oijen AM, Lee KT, Luo G, Sun H, Cherayil BJ, Kou SC & Xie XS ( 2006 ) Ever‐fluctuating single enzyme molecules: Michaelis‐Menten equation revisited. Nat Chem Biol 2, 87 – 94.en_US
dc.identifier.citedreferenceRies J & Schwille P ( 2012 ) Fluorescence correlation spectroscopy. BioEssays 34, 361 – 368.en_US
dc.identifier.citedreferenceXie XS, Yu J & Yang WY ( 2006 ) Living cells as test tubes. Science 312, 228 – 230.en_US
dc.identifier.citedreferenceRoy R, Hohng S & Ha T ( 2008 ) A practical guide to single‐molecule FRET. Nat Methods 5, 507 – 516.en_US
dc.identifier.citedreferencePitchiaya S, Androsavich JR & Walter NG ( 2012 ) Intracellular single molecule microscopy reveals two kinetically distinct pathways for microRNA assembly. EMBO Rep 13, 709 – 715.en_US
dc.identifier.citedreferenceJares‐Erijman EA & Jovin TM ( 2003 ) FRET imaging. Nat Biotechnol 21, 1387 – 1395.en_US
dc.identifier.citedreferenceGillespie DT ( 1992 ) A rigorous derivation of the chemical master equation. Phys A 188, 404 – 425.en_US
dc.identifier.citedreferenceBaras F, Pearson JE & Mansour MM ( 1990 ) Microscopic simulation of chemical oscillations in homogeneous systems. J Chem Phys 93, 5747 – 5750.en_US
dc.identifier.citedreferenceBaras F, Mansour MM & Pearson JE ( 1996 ) Microscopic simulation of chemical bistability in homogeneous systems. J Chem Phys 105, 8257 – 8261.en_US
dc.identifier.citedreferenceGrima R ( 2010 ) Intrinsic biochemical noise in crowded intracellular conditions. J Chem Phys 132, 185102.en_US
dc.identifier.citedreferenceKurtz TG ( 1972 ) Relationship between stochastic and deterministic models for chemical reactions. J Chem Phys 57, 2976 – 2978.en_US
dc.identifier.citedreferenceGrima R ( 2010 ) An effective rate equation approach to reaction kinetics in small volumes: theory and application to biochemical reactions in nonequilibrium steady‐state conditions. J Chem Phys 133, 035101.en_US
dc.identifier.citedreferenceKomorowski M, Finkenstadt B, Harper CV & Rand DA ( 2009 ) Bayesian inference of biochemical kinetic parameters using the linear noise approximation. BMC Bioinformatics 10, 343.en_US
dc.identifier.citedreferenceRoussel MR & Fraser SJ ( 1991 ) Accurate steady‐state approximations: implications for kinetics experiments and mechanism. J Phys Chem 95, 8762 – 8770.en_US
dc.identifier.citedreferenceGillespie DT ( 2007 ) Stochastic simulation of chemical kinetics. Annu Rev Phys Chem 58, 35 – 55.en_US
dc.identifier.citedreferenceJachimowski CJ, McQuarrie DA & Russell ME ( 1964 ) A stochastic approach to enzyme‐substrate reactions. Biochemistry 3, 1732 – 1736.en_US
dc.identifier.citedreferenceArányi P & Tóth J ( 1977 ) A full stochastic description of the Michaelis‐Menten reaction for small systems. Acta Biochim Biophys Acad Sci Hung 12, 375 – 388.en_US
dc.identifier.citedreferenceSchnell S & Mendoza C ( 2004 ) The condition for pseudo‐first‐order kinetics in enzymatic reactions is independent of the initial enzyme concentration. Biophys Chem 107, 165 – 174.en_US
dc.identifier.citedreferenceSchnell S ( 2013 ) Validity of the Michaelis‐Menten equation ‐ Steady‐state, or reactant stationary assumption: that is the question. FEBS J ???, ??? – ???. doi: 10.1111/febs.12564.en_US
dc.identifier.citedreferenceSchnell S & Mendoza C ( 1997 ) Closed form solution for time‐dependent enzyme kinetics. J Theor Biol 187, 207 – 212.en_US
dc.identifier.citedreferenceSchnell S & Maini PK ( 2000 ) Enzyme kinetics at high enzyme concentration. Bull Math Biol 62, 483 – 499.en_US
dc.identifier.citedreferenceHanson SM & Schnell S ( 2008 ) Reactant stationary approximation in enzyme kinetics. J Phys Chem A 112, 8654 – 8658.en_US
dc.identifier.citedreferenceRamaswamy R, González‐Segredo N, Sbalzarini IF & Grima R ( 2012 ) Discreteness‐induced concentration inversion in mesoscopic chemical systems. Nat Commun 3, 779.en_US
dc.identifier.citedreferenceKou SC, Cherayil BJ, Min W, English BP & Xie XS ( 2005 ) Single‐molecule Michaelis‐Menten equations. J Phys Chem B 109, 19068 – 19081.en_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


Files in this item

Name:
febs12663.pdf
Size:
328.8KB
Format:
PDF
View/Open

Show simple item record

Remediation of Harmful Language

The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.

Accessibility

If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.